scholarly journals Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model

Algorithms ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 22 ◽  
Author(s):  
Xingkui Xu ◽  
Chunfeng Wu ◽  
Qingyu Hou ◽  
Zhigang Fan
Keyword(s):  
Neural Network ◽  
Error Compensation ◽  
Linear Models ◽  
Arima Model ◽  
Network Models ◽  
Random Component ◽  
Neural Network Models ◽  
Angle Sensor ◽  
Elman Neural Network ◽  
Differential Algorithm

As an important angle sensor of the opto-electric platform, gyro output accuracy plays a vital role in the stabilization and track accuracy of the whole system. It is known that the generally used fixed-bandwidth filters, single neural network models, or linear models cannot compensate for gyro error well, and so they cannot meet engineering needs satisfactorily. In this paper, a novel hybrid ARIMA-Elman model is proposed. For the reason that it can fully combine the strong linear approximation capability of the ARIMA model and the superior nonlinear compensation capability of a neural network, the proposed model is suitable for handling gyro error, especially for its non-stationary random component. Then, to solve the problem that the parameters of ARIMA model and the initial weights of the Elman neural network are difficult to determine, a differential algorithm is initially utilized for parameter selection. Compared with other commonly used optimization algorithms (e.g., the traditional least-squares identification method and the genetic algorithm method), the intelligence differential algorithm can overcome the shortcomings of premature convergence and has higher optimization speed and accuracy. In addition, the drift error is obtained based on the technique of lift-wavelet separation and reconstruction, and, in order to weaken the randomness of the data sequence, an ashing operation and Jarque-Bear test have been added to the handle process. In this study, actual gyro data is collected and the experimental results show that the proposed method has higher compensation accuracy and faster network convergence, when compared with other commonly used error-compensation methods. Finally, the hybrid method is used to compensate for gyro error collected in other states. The test results illustrate that the proposed algorithm can effectively improve error compensation accuracy, and has good generalization performance.

Effect of Social Media Interactions on CLV in Telecommunications

2020 ◽  
Vol 19 (02) ◽  
pp. 447-468
Author(s):  
Oğuzhan Kivrak ◽  
Cüneyt Akar
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Network Models ◽  
Neural Network Models ◽  
Telecommunication Sector ◽  
Elman Neural Network ◽  
Customer Lifetime ◽  
Twitter Account ◽  
Network Approaches ◽  
Artificial Neural Network Models

The main goal of this study is to investigate whether social media, as a recent communication channel, has an impact on customer lifetime value (CLV). No studies have been done in Turkey with similar purposes in the telecommunication sector. To reach this goal, there has been an attempt to develop both artificial neural network models and sector-specific applicable models. Four years of data between 2011 and 2014 belonging to customers in the telecommunication sector who have a Twitter account are used in this study. The CLV is modeled through radial basis function (RBF), multilayer perceptron (MLP), and Elman neural network approaches, and the performance of such models is compared. According to the findings, calculated CLV error values are at an acceptable range in all formed models. Additionally, it is determined that the CLV was calculated with a lower error value in models where social media variables were used. The Elman neural network is determined to perform better compared to RBF and MLP.

Forecasting Cryptocurrency Prices Using ARIMA and Neural Network: A Comparative Study

2020 ◽  
Author(s):  
Saurabh Kumar
Keyword(s):  
Neural Network ◽  
Time Horizon ◽  
Moving Average ◽  
Arima Model ◽  
Network Models ◽  
Sample Period ◽  
Neural Network Models ◽  
Time Horizons ◽  
The World ◽  
Auto Regressive

The prices of cryptocurrencies are very volatile and forecasting them is a challenging task for the researchers across the world. The present study examines the accuracy of forecasted returns of the two most popular cryptocurrencies (Bitcoin and Ethereum) for the sample period spanning from October 1, 2013, to November 30, 2018. Auto-regressive integrated moving average (ARIMA) and Neural Network models have been used to forecast the returns of the cryptocurrencies. The forecasting results for different time-horizons indicate that for a shorter time-horizon, ARIMA model is better for forecasting the returns of cryptocurrencies, whereas, for a longer time-horizon, Neural Network model is better for forecasting the returns of cryptocurrencies. These results have implications for traders, investors, regulators, policymakers and academia.

CATEGORIZATION AND IDENTIFICATION OF HUMAN FACE IMAGES BY NEURAL NETWORKS: A REVIEW OF THE LINEAR AUTOASSOCIATIVE AND PRINCIPAL COMPONENT APPROACHES

1994 ◽  
Vol 02 (03) ◽  
pp. 413-429 ◽  
Author(s):  
D. VALENTIN ◽  
H. ABDI ◽  
A.J. O’TOOLE
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Processing ◽  
Linear Models ◽  
Network Models ◽  
Principal Component ◽  
Human Face ◽  
Neural Network Models ◽  
Face Images ◽  
Face Categorization

Recent statistical/neural network models of face processing suggest that faces can be efficiently represented in terms of the eigendecomposition of a matrix storing pixel-based descriptions of a set of face images. The studies presented here support the idea that the information useful for solving seemingly complex tasks such as face categorization or identification can be described using simple linear models (linear autoassociator or principal component analysis) in conjunction with a pixel-based coding of the faces.

scholarly journals Prediction of Area and Production of Groundnut Using Box-Jenkins Arima and Neural Network Approach

2020 ◽  
Author(s):  
S. T. Pavana Kumar ◽  
Ferdinand B. Lyngdoh
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Arima Model ◽  
Network Models ◽  
Arima Models ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Data Set ◽  
The Neural Network ◽  
Hidden Layer

Selection of parameters for Auto Regressive Integrated Moving Average (ARIMA) model in the prediction process is one of the most important tasks. In the present study, groundnut data was utlised to decide appropriate p, d, q parameters for ARIMA model for the prediction purpose. Firstly, the models were fit to data without splitting into training and validation/testing sets and evaluated for their efficiency in predicting the area and production of groundnut over the years. Meanwhile, models are compared among other fitted ARIMA models with different p, d, q parameters based on decision criteria’s viz., ME, RMSE, MAPE, AIC, BIC and R-Square. The ARIMA model with parameters p-2 d-1-2, q-1-2 are found adequate in predicting the area as well as production of groundnut. The model ARIMA (2, 2, 2) and ARIMA (2,1,1) predicted the area of groundnut crop with minimum error estimates and residual characteristics (ei). The models were fit into split data i.e., training and test data set, but these models’ prediction power (R-Square) declined during testing. In case of predicting the area, ARIMA (2,2,2) was consistent over the split data but it was not consistent while predicting the production over years. Feed-forward neural networks with single hidden layer were fit to complete, training and split data. The neural network models provided better estimates compared to Box-Jenkins ARIMA models. The data was analysed using R-Studio.

scholarly journals Comparison of Non Linear Models and Artificial Neural Network to Describe the Liveweight from Birth to Maturity in Female Jersey Cattle

2021 ◽  
pp. 114-119
Author(s):  
D. O. Omoniwa ◽  
J. E. T. Akinsola ◽  
R. O. Okeke ◽  
J. M. Madu ◽  
D. S. Bunjah Umar
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Inflection Point ◽  
Linear Models ◽  
Network Models ◽  
Linear Functions ◽  
Growth Data ◽  
Neural Network Models ◽  
Jersey Cattle ◽  
Non Linear

Evaluation of growth data is an important strategy to manage gross feed requirement in female Jersey cattle in the New Derived Guinea Savannah Zone of Nigeria. Two non-linear functions (Gompertz and Logistic) and Neural network models were used to fit liveweight (LW)-age data using the non linear procedure of JMP statistical software. Data used for this study were collected from 150 Jersey female cattle in Shonga Dairy Farm, Kwara, State from 2010-2018. The Neural network function showedthe best goodness of fit. Both the Gompertz and Logistic functions overestimated LW at birth, 3, 36, 48, 60 and 72months respectively. NN function overestimated the LW at 0, 3, 24, 36 and 72 months. The Gompertzfunction had the best estimation of asymptotic weight (649.51 kg) with average absolute growth rate (0.061 kg/day).The inflection point was 15.95, 9.55 and 34.5 months in Logistic, Gompertz and neural network models, respectively. A strong and positive correlation was observed between asymptote and inflection point in Gompertz functions. The metrics of goodness of fit criteria (R2 and RMSE), showed that NN with multilayer perceptron was superior to the other models but Gompertz model, was best in its ability to approximate complex functions of growth curve parametersin female Jersey cattle.

scholarly journals Artificial Neural Network Models for Rainfall Prediction

2021 ◽  
Vol 5 (2) ◽  
pp. 30-35
Author(s):  
Emmanuel Gbenga Dada ◽  
Hurcha Joseph Yakubu ◽  
David Opeoluwa Oyewola
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Climatic Conditions ◽  
Feed Forward Neural Network ◽  
Neural Network Models ◽  
Elman Neural Network ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Nonlinear Nature

Rainfall prediction is an important meteorological problem that can greatly affect humanity in areas such as agriculture production, flooding, drought, and sustainable management of water resources. The dynamic and nonlinear nature of the climatic conditions have made it impossible for traditional techniques to yield satisfactory accuracy for rainfall prediction. As a result of the sophistication of climatic processes that produced rainfall, using quantitative techniques to predict rainfall is a very cumbersome task. The paper proposed four non-linear techniques such as Artificial Neural Networks (ANN) for rainfall prediction. ANN has the capacity to map different input and output patterns. The Feed Forward Neural Network (FFNN), Cascade Forward Neural Network (CFNN), Recurrent Neural Network (RNN), and Elman Neural Network (ENN) were used to predict rainfall. The dataset used for this work contains some meteorological variables such as temperature, wind speed, humidity, rainfall, visibility, and others for the year 2015-2019. Simulation results indicated that of all the proposed Neural Network (NN) models, the Elman NN model produced the best performance. We also found out that Elman NN has the best performance for the year 2018 (having the lowest RMSE, MSE, and MAE of 6.360, 40.45, and 0.54 respectively). The results indicated that NN algorithms are robust, dependable, and reliable algorithms that can be used for daily, monthly, or yearly rainfall prediction.

Identification of bolt anchorage defects based on Elman neural network optimised by improved chicken swarm optimisation algorithm

2020 ◽  
Vol 62 (10) ◽  
pp. 588-597
Author(s):  
Weiguo Di ◽  
Mingming Wang ◽  
Xiaoyun Sun ◽  
Guang Han ◽  
Hui Xing
Keyword(s):  
Neural Network ◽  
Frequency Response ◽  
Network Models ◽  
Identification Accuracy ◽  
Response Functions ◽  
Neural Network Models ◽  
Deep Foundation ◽  
Elman Neural Network ◽  
Opposition Based Learning ◽  
Identification Model

Rock bolts play an important supporting role in the construction of slopes, deep foundation pits and tunnels. As such, it is especially necessary to assess bolt anchorage quality. This paper proposes an identification model for bolt anchorage defects based on an Elman neural network (ElmanNN) optimised using an improved chicken swarm optimisation (CSO) algorithm and the frequency response function. First, the principal components of the frequency response functions of different anchorage bolts are used as the input within the model. Next, the weights and thresholds of the ElmanNN are optimised using an improved CSO algorithm based on chaotic disturbance and elite opposition-based learning. Finally, the model is used to identify bolt anchorage defects. The experimental results show that the model has a higher identification accuracy and faster convergence rate than other neural network models.

scholarly journals Convolutional neural network models of V1 responses to complex patterns

2018 ◽  
Author(s):  
Yimeng Zhang ◽  
Tai Sing Lee ◽  
Ming Li ◽  
Fang Liu ◽  
Shiming Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Linear Models ◽  
Network Models ◽  
Large Set ◽  
Key Factors ◽  
Neural Network Models ◽  
V1 Neurons ◽  
Complex Patterns ◽  
Neuron Modeling

AbstractIn this study, we evaluated the convolutional neural network (CNN) method for modeling V1 neurons of awake macaque monkeys in response to a large set of complex pattern stimuli. CNN models outperformed all the other baseline models, such as Gabor-based standard models for V1 cells and various variants of generalized linear models. We then systematically dissected different components of the CNN and found two key factors that made CNNs outperform other models: thresholding nonlinearity and convolution. In addition, we fitted our data using a pre-trained deep CNN via transfer learning. The deep CNN’s higher layers, which encode more complex patterns, outperformed lower ones, and this result was consistent with our earlier work on the complexity of V1 neural code. Our study systematically evaluates the relative merits of different CNN components in the context of V1 neuron modeling.

scholarly journals Forecasting stock index based on hybrid artificial neural network models

2019 ◽  
Vol 3 (1) ◽  
pp. 52-57
Author(s):  
Ta Quoc Bao ◽  
Le Nhat Tan ◽  
Le Thi Thanh An ◽  
Bui Thi Thien My
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Arima Model ◽  
Network Models ◽  
Stock Index ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural

Forecasting stock index is a crucial financial problem which is recently received a lot of interests in the field of artificial intelligence. In this paper we are going to study some hybrid artificial neural network models. As main result, we show that hybrid models offer us effective tools to forecast stock index accurately. Within this study, we have analyzed the performance of classical models such as Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) model and the Hybrid model, in connection with real data coming from Vietnam Index (VNINDEX). Based on some previous foreign data sets, for most of the complex time series, the novel hybrid models have a good performance comparing to individual models like ARIMA and ANN. Regarding Vietnamese stock market, our results also show that the Hybrid model gives much better forecasting accuracy compared with ARIMA and ANN models. Specifically, our results tell that the Hybrid combination model delivers smaller Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) than ARIMA and ANN models. The fitting curves demonstrate that the Hybrid model produces closer trend so better describing the actual data. Via our study with Vietnam Index, it is confirmed that the characteristics of ARIMA model are more suitable for linear time series while ANN model is good to work with nonlinear time series. The Hybrid model takes into account both of these features, so it could be employed in case of more generalized time series. As the financial market is increasingly complex, the time series corresponding to stock indexes naturally consist of linear and non-linear components. Because of these characteristic, the Hybrid ARIMA model with ANN produces better prediction and estimation than other traditional models.  

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